Performance comparisons between machine learning and analytical models for quality of transmission estimation in wavelength-division-multiplexed systems [Invited]

We conduct a comprehensive comparative study of quality-of-transmission (QoT) estimation for wavelength-division-multiplexed systems using artificial neural network (ANN)-based machine learning (ML) models and Gaussian noise (GN) model-based analytical models. To obtain the best performance for comparison, we optimize all the system parameters for GN-based models in a brute-force manner. For ML models, we optimize the number of neurons, activation function, and number of layers. In simulation settings with perfect knowledge of system parameters and communication channels, GN-based analytical models generally outperform ANN models even though GN models are less accurate on the side channels due to the local white-noise assumption. In experimental settings, however, inaccurate knowledge of various link parameters degrades GN-based models, and ML generally estimates the QoT with better accuracy. However, ML models are temporally less stable and less generalizable to different link configurations. We also briefly study potential network capacity gains resulting from improved QoT estimators and reduced operating margins.